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LatentDiff is proposed, a novel unified latent generative framework for TSP, which reformulates knowledge graph completion as latent space graph generation and introduces an observation-anchored latent graph diffusion model.
Discrete Diffusion Models (DDMs) have recently been introduced to recommendation systems, modeling user history as a token generation process via iterative denoising. However, while effective at capturing user-level sequential patterns, these methods often fail to explicitly integrate item-based collaborative filtering...
Results indicate that discrete, anchor-constrained latent modeling provides a powerful and biologically coherent solution for unpaired single-cell multi-omics integration, and scCoA-VQA can accurately capture the meaningful regulatory structure.
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